Triple

T7030668
Position Surface form Disambiguated ID Type / Status
Subject H. Halberstam E163260 entity
Predicate givenName P17 FINISHED
Object Heilbronn E296707 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Heilbronn | Statement: [H. Halberstam, givenName, Heilbronn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heilbronn
Context triple: [H. Halberstam, givenName, Heilbronn]
  • A. Heilbronn chosen
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • B. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • C. Ulm
    Ulm is a historic city in the German state of Baden-Württemberg, best known for its towering Gothic cathedral and as the birthplace of physicist Albert Einstein.
  • D. Schweinfurt
    Schweinfurt is a city in northern Bavaria, Germany, historically known for its ball bearing industry and as a strategic target during World War II.
  • E. Stuttgart
    Stuttgart is a major city in southwestern Germany known as an important industrial, cultural, and economic center, particularly famous for its automotive industry and surrounding wine-growing region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c6885d691c81908cf7d31083113886 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e20dbc8c8190a7446290747d8078 completed March 27, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb59a7732c819095aa0903d419b740 completed March 31, 2026, 5:20 a.m.
Created at: March 27, 2026, 2:35 p.m.